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On Bootstrapping Using Smoothed Bootstrap
International Congress on Mathematical Software, 2019The standard bootstrap method was introduced as a resampling method for statistical inference; it is a computer based method for assigning measures of accuracy to statistical estimates. The bootstrap sample is obtained by randomly sampling n times, with replacement, from the original sample.
Sulafah Binhimd
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Optimizing the smoothed bootstrap
Annals of the Institute of Statistical Mathematics, 1995zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Suojin Wang
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On the bootstrap and smoothed bootstrap
Communications in Statistics - Theory and Methods, 1989The standard bootstrap and two commonly used types of smoothed bootstrap are investigated. The saddlepoint approximations are used to evaluate the accuracy of the three bootstrap estimates of the density of a sample mean. The optimal choice for the smoothing parameter is obtained when smoothing is useful in reducing the mean squared error.
Suojin Wang
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Smoothed Bootstrap for Survival Function Inference
2019 International Conference on Information and Digital Technologies (IDT), 2019A new generalized smoothed bootstrap technique is presented for data including right-censored observations. The method is based on Banks’ bootstrap [2] and the right-censoring A(n) assumption introduced by [7], which is a generalization of Hill’s A(n ...
A. S. A. Al Luhayb +2 more
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Journal of Statistical Planning and Inference, 2000
Let \(X_1,X_2,\dots, X_n\) be a sample of independent and identically distributed random variables. Denote by \(F\) the distribution function of \(X\). The authors consider the problem of estimating the parameter \(T(F)\) by a statistic \(T(\widehat F_n)\), where \(\widehat F_n(x)= \int^x_{-\infty} f_n(u) du\) and \(f_n(u)\) is the well-known kernel ...
C. El-Nouty, A. Guillou
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Let \(X_1,X_2,\dots, X_n\) be a sample of independent and identically distributed random variables. Denote by \(F\) the distribution function of \(X\). The authors consider the problem of estimating the parameter \(T(F)\) by a statistic \(T(\widehat F_n)\), where \(\widehat F_n(x)= \int^x_{-\infty} f_n(u) du\) and \(f_n(u)\) is the well-known kernel ...
C. El-Nouty, A. Guillou
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On multivariate smoothed bootstrap consistency
Journal of Statistical Planning and Inference, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
D. Martini, Fabio Rapallo
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On smoothed bootstrap for density functionals
Journal of Nonparametric Statistics, 2003We analyze, from both theoretical and practical point of view, the use of the smoothed bootstrap in the estimation of a functional T(f) of the underlying density. We consider a plug-in approach based on the use of an estimator of type T(fˆ n ) where fˆ n is a nonparametric (kernel) estimator of f.
A. Alonso, A. Cuevas
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Bandwidth selection for the smoothed bootstrap percentile method
Computational Statistics & Data Analysis, 2001Some applications of the bootstrap involve smoothing the estimated distribution that is resampled, a method known as the smoothed bootstrap. Recently, the effect of resampling a kernel smoothed distribution was evaluated through expansions for the coverage of bootstrap percentile confidence intervals.
Alan M. Polansky
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Smoothed bootstrap confidence intervals with discrete data
Computational Statistics & Data Analysis, 1997zbMATH Open Web Interface contents unavailable due to conflicting licenses.
R. Guerra +2 more
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Importance resampling for the smoothed bootstrap
Journal of Statistical Computation and Simulation, 1992Alternative methods of estimating properties of unknown distributions include the bootstrap and the smoothed bootstrap. In the standard bootstrap setting, Johns (1988) introduced an importance resam¬pling procedure that results in more accurate approximation to the bootstrap estimate of a distribution function or a quantile.
Zehua Chen, K. Do
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